Baolu Shen

I was CPO at Blend before their $4 billion IPO. We rejected the “forward deployed engineer” Palantir model that everyone is copying now. Here’s why: Blend could have followed the forward-deployed engineer approach. Both our CEO and CTO were early Palantir. They'd seen it work. Instead, we built a SaaS product with core components. Move fast, iterate, use data to learn. Not custom code for every client. That Palantir model is having a resurgence. Now Sierra, Decagon, and others are hiring forward-deployed engineers. But it’s not the right approach for every company. It’s fundamentally the wrong answer for Assembled’s customers. Here's what we understand about support teams and why we’re not going the forward deployed route: Customer Support teams are used to getting no resources and no support. On the surface, you'd think they'd be excited about engineers building custom solutions. "Finally, someone's offering us developers!" The reality is support teams are terrified of getting help they can't afford to keep. Support teams are wary because they know the math. They don't have budget for expensive, scarce resources. At some point the shoe drops. The engineers disappear when priorities shift. And custom solutions break. Support is left holding the bag with something they can't maintain. They're resource-starved by design. The idea of a dependency on resources they can't control is terrifying. So instead, the goal should be to give support teams the ability to manage, change, and solve their own problems. Not dependencies on engineers they can't keep. Smart, competent people who teach them to do it themselves. Palantir wasn't even a product company for years. They were a custom development shop with reusable components. Show up, build something bespoke, maybe extract learnings. That's not scalable for any but the largest support teams who need sustainability. Forward-deployed engineers are the new shiny object. I wasn’t a fan of it at Blend. And I’m not a fan today either. What I see in the customer support space is that teams need empowerment, not dependency. I want the teams we work with to be able to say: "I love that you’re partnering with me. And I love that I can do this myself too." That's the difference between building a product and building a consulting practice.

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✨ A very fun start to Frameworks for Growth - a new video series from Vanta. "Frameworks" are core to who Vanta is: -We support 40 different compliance frameworks within the product -One of our principles is "Decide with frameworks," and a common question in meetings at Vanta is, "what framework did you use for deciding [x]?" -And now, we're hoping to provide founders and early operators with some frameworks *they* can use for scaling their businesses in the age of AI. Thanks to Garry Tan and Christina Cacioppo for this awesome first conversation. You should watch the whole thing, but tldw - one framework for growth: learn an industry deeply and vibe code towards something people want.

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Most companies still treat product partnerships as “ship the integration, issue a press release & move on to the next.” Asher Mathew and I had a great convo with Erika Wool - who leads Product & Strategic Partnerships at industry-leader Stripe - about how to counteract that mindset and drive real ecosystem-driven growth. Here are a few insights from our convo 👇 🔹 1. Product partnerships should create businesses not just applications. Stripe builds products that literally cannot exist without partners: payment methods, banks, financial infrastructure, AI experiences, and more. If your partnerships don’t meaningfully shape how your product drives value, it’s not strategic. It’s a logo. 🔹 2. Launch is the starting line, not the finish line. Erika talked about the “care and feeding” after launch: SLAs, QBRs, joint roadmaps, new markets, new features, constant recalibration. If your integration degrades as products evolve from benign neglect, your partnership won't keep paying dividends. 🔹 3. The best partner leaders speak “product,” “strategy,” and “finance.” You must connect market trends, user data, and long-term economics into a story that executives (especially CFOs) can get behind. If you can’t articulate the financial arc of a partnership, you’re flying blind and you won't maintain internal support. 🔹 4. Saying “no” is a superpower. Partnerships generate endless opportunities. The leaders who win are the ones who stay focused and keep their product teams focused. 🔹 5. Internal bridges matter as much as the external ones. Engineering, product, sales, channel, CS, marketing: everyone touches the same partner for different reasons. The job isn’t to “own” the partner … it’s to prevent the org chart from being what the partner experiences and fracturing customer value. 🔹 6. AI is turning every company into a tech company and partnerships will only get more central. As Erika put it, we’ll need to watch not just the traditional tech ecosystem, but retail, airlines, healthcare, etc., because product partnerships are becoming the infrastructure beneath all modern business and consumer experiences. This conversation is one of the clearest explanations I’ve heard of what strategic product partnerships actually look like: messy, cross-functional, multidimensional, and essential now more than ever. If your company is trying to move from “we launched an integration and a press release” to “we built a business around an integrated ecosystem driving customer value,” check out the full convo with Erika here: https://lnkd.in/ewJgn32f (Also hat tip for Glen Roth for the intro to Erika after I read his interview of Erika in Glenn's newsletter - I've been a subscriber for 5 years - it's a must read if you are in partnerships 💰)

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Had such a great chat with Vivien Lin! She brought so much wisdom and vivid insights to the conversation: 1. We're trying to work with our users, when they make money we make money. We try not to stand on the opposite. 2. From the start we decided to exit China market. We never touched US users. It's all because we are quite self-disciplined and self-regulated. 3. We consider whether this product or whether a feature can truly solve the problem for the user. 4. From 2018, 2019 is always put our top priority to think about how we bring more new user to the crypto industry. It's not like competing the existing user with other exchange. 5. Ideally, the final product should be you don't need to worry about what functions sit in what page. You just go to the AI or the AI assistant to tell him what you want. 6. The difference in the roadmap between crypto and traditional finance is rooted in how the industry got originated. For traditional finance, banking and payment that's naturally where finance comes from than lending than trading. At crypto, the very first need where the industry originated is mining. 7. I think, we are still in a very precious time window especially for exchange it to become a sort of mega platform in near future. 8. My suggestion is always be open minded - open minded to the technology, business model and even all those new ideas. 9. My suggestion to everyone, even if you are a legal, you are a product manager or you are in the operation team, is to study programming, learn how to code. 10. The slides always tell the good stories but you need to get down to the details to understand what's happening. 11. Before all those regulations put into place there has to be someone familiar with how possible the regulation is going to be implemented in the future and we can get prepared beforehand. 12. Without one major hack exchange cannot be a mature exchange. 13. If I know what they are doing I can read their code, I can get a very direct sense about the quality of the project instead of really looking at the powerpoint. Watch the full session here: https://lnkd.in/dGhbXdwr BingX

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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Just watched a fantastic interview with Andy Jassy by Harvard Business Review—well worth your time. (Link + transcript in comments if you want to run it through your own LLM summarizing tools.) What really struck me was how Amazon, even at its massive scale, deliberately wants to operate like a startup and has a clear vision on what the AI playing field looks like. A few insights stood out that I think are especially relevant: 🤖 The AI playing field Amazon is taking a layered approach to AI, investing in its own chips (Trainium and Inferentia) to optimize performance and cost at the infrastructure level, using well established tools like Sagemaker to test and prototype models. At the application layer, tools like Rufus show how generative AI is being embedded directly into customer experiences. There won’t be one model to rule them all. Companies will choose based on what they need—accuracy, latency, cost, or size. 🔍 Solving Real Problems A valuable reminder: don't get too attached to your own technology or roadmaps. The real measure is whether we’re solving remarkable customer problems. “Startups are missionary about trying to solve problems for customers.” 🛠️ The Builder Personality “We need builders—people who want to invent, who get energized by deeply understanding customer experiences and reimagining them. Not just building for the sake of tech, but solving real problems.” Builders are energized by the “why” behind customer needs—and act on it. 🧠 The Owner Mentality “You need people to think, what would I do if this were my money? I own this piece of the problem—even if the rest isn't clear, it’s my job to solve it.” Accountability and initiative like that can’t be taught in a meeting—it has to be part of the culture. 🚪 Revisiting Two-Way Doors As companies grow, decisions slow. People hesitate, afraid of failure. Jassy reminds us that many decisions are reversible—“two-way doors.” Letting teams move fast on those can stop paralysis from setting in. 📌 And one quote that hits hard for org design: “You end up with things like a pre-meeting for the pre-meeting for the decision meeting. Or owners who don’t feel they can own the decision anyway.” That's not a company you want to end up in!

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